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The most popular blog posts about ai & machine learning on Hacker News. 6,331 posts.
I've been working for a while on a program that automatically generates analog electronic circuits using evolutionary algorithms. It's still very much a work in progress, but I've used it to generate some useful circuits. Current workings of the program is as follows: Programmer decides a goal function, possible…
I had a really enlightening talk with Daniel Slutsky this week (who is an exceptional data scientist, software engineer, and community organizer I highly…
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Reviewing the few changes in the Claude 4.0 system prompts, we get a sense for how system prompts program chatbot applications.
The lottery ticket hypothesis explains why massive neural networks succeed despite centuries of theory predicting they should fail
What if you could let Claude and Codex work together as pair programmers, talking to each other directly? One of them as the main worker and the other as a reviewer.
Intel Newsroom delivers the latest news, announcements, and updates. Stay informed on Intel innovations.
Fully discrete methods dominated for a few years, but language models based on continuous diffusion are making a comeback.
Video-to-audio research uses video pixels and text prompts to generate rich soundtracks
Why network effects and distribution will be king, once more
We explore language models that recursively call themselves or other LLMs before providing a final answer. The aim is essentially unbounded context, and a way through context rot.
Gradient descent is the preferred way to optimize neural networks and many other machine learning algorithms but is often used as a black box. This post explores how many of the most popular gradient-based optimization algorithms such as Momentum, Adagrad, and Adam actually work.
Deep Reinforcement Learning (DeepRL) has achieved remarkable success in a range of tasks, from continuous control problems in robotics to playing games like Go and Atari. The improvements seen in these domains have so far been limited to individual tasks where a separate agent has been tuned and trained for each task.
MLflow is an open-source platform to manage the ML lifecycle, offering tools for data preparation, model training, and deployment at scale.
In this article, we'll use Quilt to transfer versioned training data to a remote machine. We'll start with the Berkeley Segmentation Dataset, package the dataset, then train a PyTorch model for super-resolution imaging.
What nginx logs actually prove about AI traffic from ChatGPT, Claude, and Perplexity, plus why Google's AI crawlers leave a different signal than the others.
Today Lyft is excited to announce the open sourcing of Flyte, a structured programming and distributed processing platform for highly…
Introduction 3D Gaussian splatting is a technique that answers this question: given a dataset of pictures of a scene, how can I reconstruct it in 3D? This is achieved using a machine learning algorithm that, for many different camera angles, does the following: render the scene, compare it to the picture taken at the…
Posted by Thomas Müller, Software Engineer, Google Research Much of the world’s information is stored in the form of tables, which can be found o...
This blog post is about the ACL 2017 paper Get To The Point: Summarization with Pointer-Generator Networks by Abigail See, Peter J Liu, and Christopher Manni...
In the last couple of years, machine learning has opened up new horizons in a wide range of industries, with advanced use cases emerging: Facebook’s facial recognition, Netflix’s recommended movies, PrismaAI’s image style transfer, Siri’s voice recognition, Google Allo’s natural… Continue reading 25 Open-Source…
Here’s a Mastodon thread from a year ago. Just a quick check on how ChatGPT’s getting on … Me: Who reassigned the species Brachiosaurus brancai to its own genus, and when? ChatGPT…
I define "intelligence" as being capable of knowing or understanding, at least within some domain. ChatGPT cannot know or understand anything, so it is not intelligence. It does not know what its output means. It has no idea that words can mean anything.
Over the last several months I have been tracking AI guys trying to create vibe coded clones of popular apps, especially Adobe’s apps which have always been the most pirated apps since the 90s. There are now about a dozen vibe coded attempts to clone Photoshop and some other Adobe apps, not incl
Nobody wants “temperamental and mysterious” products, so what will AI-integrated software development look like?
